Improvement of Differential Evolution Multiobjective Optimization Algorithm Based on Decomposition

Multi-objective optimization problem (MOP) is a challenging field of scientific research in real-life. The effective way to solve multi-objective optimization problems is Multi-objective Evolutionary Algorithm (MOEA). In this paper, enhancements to a Multi-objective Evolutionary algorithm MOEA/D-DE...

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Veröffentlicht in:Journal of physics. Conference series Jg. 1213; H. 3; S. 32011 - 32017
Hauptverfasser: Han, Jiaxin, He, Manman, Wang, Xiaoxiao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bristol IOP Publishing 01.06.2019
Schlagworte:
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Monte Carlo simulation
Multiple objective analysis
Optimization
Population distribution
ISSN:1742-6588, 1742-6596
Online-Zugang:Volltext
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Zusammenfassung:Multi-objective optimization problem (MOP) is a challenging field of scientific research in real-life. The effective way to solve multi-objective optimization problems is Multi-objective Evolutionary Algorithm (MOEA). In this paper, enhancements to a Multi-objective Evolutionary algorithm MOEA/D-DE are proposed. The proposed improvement points help to improve both population distribution and algorithmic local search capabilities. In an existing study, in order to better distribute the population, the Monte Carlo method was used for population initialization. Adaptive differential evolution operators are used to improve the local search ability of the algorithm. The algorithm was tested on widely used ZDT and DTLZ family test problems. The experimental results show that the proposed algorithm is better than MOEA/D-DE and has better performance than other excellent multi-objective algorithms.
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1213/3/032011